Internet of Things data asset assessment method based on multi-period excess income

Abstract

The invention discloses an Internet of Things data asset assessment method based on multi-period excess income, and relates to the technical field of data asset assessment, and the method comprises the steps: firstly determining a basic item, an income period and an enterprise free cash flow of data asset assessment, and carrying out the calculation of an asset contribution value through combining the enterprise cash flow, a fixed asset contribution value of an enterprise, and a flowing asset contribution value; calculating the discount rate of the data assets through a weighted average capital cost model and a return rate splitting method, and obtaining the initial value of the data assets in combination with the asset contribution value; calculating the step value of the data assets by using an analytic hierarchy process according to the Internet of Things data value evaluation index system; and finally, obtaining a final value by combining the initial value and the stepped value of the data assets. According to the invention, an enterprise can make a more accurate data asset decision according to scientific data analysis, and the competitiveness in a highly uncertain market environment is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset evaluation, and in particular to an Internet of Things data asset evaluation method based on multi-period excess returns. Background Art

[0002] As a fundamental and strategic resource, data is becoming increasingly valuable. IoT data, due to its massive scale, rapid generation, and wide coverage across industries, is a crucial research area for data governance, services, and development.

[0003] Currently, traditional methods for valuing data assets mainly include the cost approach, the market approach, and the income approach. However, all of these approaches have certain limitations. As the data factor market is in its infancy, there are fewer comparable companies to choose from, and financial information on comparable transaction cases is difficult to obtain. Therefore, the market approach is not applicable due to limited conditions. The cost approach also has limitations because the cost inputs of data assets are difficult to accurately measure. The existing income approach struggles to separate the income generated by data assets from the total income of the assets, and selecting an appropriate discount rate is difficult. The approach is relatively subject to subjective factors, and its objectivity needs to be improved.

[0004] Therefore, it is of great significance to design an effective data asset evaluation method. Summary of the Invention

[0005] The purpose of the present invention is to provide an IoT data asset evaluation method based on multi-period excess returns to address the shortcomings of the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an IoT data asset evaluation method based on multi-period excess returns, comprising:

[0007] The basic items for determining data asset evaluation include data items, the time span of the data, and market information;

[0008] Determine the revenue period of data assets and the company's free cash flow;

[0009] Calculate the fixed asset contribution value and current asset contribution value of the enterprise to obtain the data asset contribution value;

[0010] Calculate the discount rate of data assets using the weighted average cost of capital model and the rate of return split method;

[0011] Calculate the initial value of data assets;

[0012] Use the analytic hierarchy process and the IoT data value assessment index system to calculate the ladder value of data assets;

[0013] Calculate the final value of data assets.

[0014] In a preferred embodiment, the steps of determining the basic items of data asset evaluation including data items, data time span, and market information are as follows:

[0015] Determine the data items including the asset type and asset status of the enterprise, where asset types include fixed assets, current assets, and data assets;

[0016] Determine the time span of selected data based on evaluation requirements;

[0017] Determining market information includes market research and customer needs and trends.

[0018] In a preferred embodiment, the steps of determining the income period of data assets and the free cash flow of the enterprise are:

[0019] Collect data from historical data assets and analyze the data through recurrent neural networks to determine the return period of the data assets;

[0020] Obtain the company's tax rate TR and its earnings before interest and taxes (EBIT), depreciation and amortization (D&A), working capital increase (ΔWC), and capital expenditure (CAPEX) during its operations;

[0021] The formula for calculating the company's free cash flow FCF is:

[0022] FCF=EBIT×(1-TR)+D&A-ΔWC-CAPEX

[0023] In a preferred embodiment, the step of calculating the fixed asset contribution value and the current asset contribution value of the enterprise to obtain the data asset contribution value is as follows:

[0024] Define the fixed asset contribution value C f , Fixed asset loss compensation F m , Return on Fixed Asset Investment F p , Depreciation of existing fixed assets F a , Depreciation of newly purchased fixed assets F n , expected fixed asset value F v , fixed asset investment rate of return F i , Current assets contribution value C m , expected current asset value M v , Return on Investment of Current Assets M r ;

[0025] The fixed asset contribution value is: C f =F m +F p ;

[0026] Among them, F m =F a +Fn , F p =F v +F i , and F i The bank loan interest rate for a preset number of years;

[0027] The contribution of current assets is: C m =M v ×M r ;

[0028] Among them, M r The bank loan interest rate is set at a predetermined number of years based on the current asset turnover cycle;

[0029] Based on the enterprise's free cash and fixed asset contribution value C f and current asset contribution value C m Calculate the data asset contribution value C p :

[0030] C p =FCF-C f -C m

[0031] In a preferred embodiment, the step of calculating the discount rate of data assets by using the weighted average cost of capital model and the rate of return split method is:

[0032] Weighted Average Cost of Capital Formula To calculate the weighted average cost of capital:

[0033]

[0034] Among them, WACC is the weighted average cost of capital, K r is the cost of equity capital, K d is the cost of debt capital, H is the value of common stock, Z is the total amount of long-term and short-term loans, and T is the corporate income tax rate;

[0035] The investment discount rate of data assets is calculated based on the weighted average cost of capital using the return split rate method:

[0036]

[0037] where φ f ,φ c ,φ i are the weights of fixed assets, current assets and data assets in total assets, γ f , γ c , γ i They are the return on investment of fixed assets, current assets and data assets respectively.

[0038] In a preferred embodiment, the step of calculating the initial value of the data asset is:

[0039] The initial value V of data assets is calculated by integrating the enterprise's free cash flow, fixed asset contribution value, current asset contribution value, data asset contribution value and data asset investment return rate. i ;

[0040]

[0041] Among them, V i is the initial value of data assets, (FCF-C f -C m ) t is the contribution value of data assets in year t, γ i is the data asset discount rate.

[0042] In a preferred embodiment, the steps of using the analytic hierarchy process and calculating the ladder value of data assets based on the Internet of Things data value assessment index system are as follows:

[0043] Use the analytic hierarchy process to construct a data asset value assessment index system table;

[0044] The evaluation index system table includes the target layer, the quasi-measurement layer and the solution layer;

[0045] The target layer is the value of data assets, and the measurement layer includes data quality, data application, and data usage risks;

[0046] Data quality includes accuracy, completeness, and data cost; data application includes scarcity, timeliness, diversity, industry characteristics, frequency of use, and versatility; and data use risks include legal restrictions and ethical constraints.

[0047] According to the evaluation index system, each pair of indicators is compared using a scale of 1 to 9 to construct a comparison matrix;

[0048] Calculate the maximum eigenvalue and consistency index θ1 of the comparison matrix;

[0049] The consistency index θ1 is:

[0050]

[0051] Among them, λ max is the maximum eigenvalue of the contrast matrix, n is the order of the contrast matrix;

[0052] Define contrast matrices of different orders to set preset ratios R1;

[0053] Calculate the random consistency index θ using the consistency index θ1 and the preset ratio R :

[0054]

[0055] If the random consistency index θ R If the consistency threshold is exceeded, it means that the evaluation index system table and comparison matrix have passed the consistency verification; otherwise, it fails and the evaluation index system table and comparison matrix are rebuilt;

[0056] Calculate the indicator score and indicator weight of each indicator at the quasi-measurement layer and the solution layer using the consistency-verified evaluation indicator system table and comparison matrix;

[0057] Calculate the ladder value of data assets based on comprehensive indicator scores and indicator weights:

[0058]

[0059] Among them, V s is the ladder value of data assets, is the weight of each indicator, S j Assign a score to each indicator.

[0060] In a preferred embodiment, the step of calculating the final value of the data asset is:

[0061] The final value of data assets is calculated based on the initial value of data assets and the ladder value of data assets:

[0062]

[0063] Among them, V F The ultimate value of data assets, It is the correction coefficient of data asset value.

[0064] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0065] 1. The present invention provides a systematic framework for data asset evaluation through clear steps and detailed calculation models. Specifically, the evaluation process includes determining data items, time spans, market information, and calculating the contribution value and discount rate of various assets of the enterprise. Such a structured process effectively reduces the subjectivity in the evaluation process, making the evaluation results more objective and reliable. At the same time, by using the multi-period excess return method, the free cash flow of the enterprise in the next few years can be comprehensively considered to obtain a more accurate initial value and final value of the data assets. This systematic evaluation methodology enables enterprises to make more accurate asset decisions based on scientific data analysis, thereby enhancing their competitiveness in a highly uncertain market environment. Especially for IoT enterprises, the value of data assets and their changing trends are particularly important;

[0066] 2. The present invention has significant advantages in evaluating the ladder value of data assets by adopting the hierarchical analysis method, which is particularly reflected in the systematic and quantitative decision support. The hierarchical analysis method makes the evaluation process more systematic by decomposing complex problems into multiple levels. This structured approach helps decision makers clearly understand the relationship between various factors and maintain a comprehensive perspective when considering different indicators. In addition, the hierarchical analysis method makes the evaluation process more quantitative by establishing a comparison matrix and calculating priority weights. This quantification not only improves the objectivity of the evaluation results, but also reduces subjective bias through the introduction of mathematical models, making decisions more scientific. Evaluators can use consistency checks to ensure the rationality of the constructed evaluation system and its weights, further improving the reliability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0068] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0070] Example 1, please refer to Figure 1 As shown, the IoT data asset evaluation method based on multi-period excess returns described in this embodiment includes:

[0071] S1. Determine the basic items for data asset evaluation, including data items, data time span, and market information;

[0072] S2. Determine the income period of data assets and the enterprise's free cash flow;

[0073] S3. Calculate the fixed asset contribution value and current asset contribution value of the enterprise to obtain the data asset contribution value;

[0074] S4. Calculate the discount rate of data assets using the weighted average cost of capital model and the rate of return split method;

[0075] S5. Calculate the initial value of data assets;

[0076] S6. Use the analytic hierarchy process and the IoT data value assessment index system to calculate the ladder value of data assets;

[0077] S7. Calculate the final value of data assets;

[0078] As described in steps S1-S7 above, data, as a fundamental and strategic resource, is becoming increasingly valuable. IoT data, due to its massive scale, rapid generation, and wide coverage across industries, is a crucial research area for data governance, services, and development.

[0079] Currently, traditional methods for valuing data assets mainly include the cost approach, the market approach, and the income approach. However, all of these approaches have certain limitations. As the data factor market is in its infancy, there are fewer comparable companies to choose from, and financial information on comparable transaction cases is difficult to obtain. Therefore, the market approach is not applicable due to limited conditions. The cost approach also has limitations because the cost inputs of data assets are difficult to accurately measure. The existing income approach struggles to separate the income generated by data assets from the total income of the assets, and selecting an appropriate discount rate is difficult. The approach is relatively subject to subjective factors, and its objectivity needs to be improved.

[0080] The present invention provides a systematic framework for data asset evaluation through clear steps and detailed calculation models. Specifically, the evaluation process includes determining data items, time spans, market information, and calculating the contribution value and discount rate of various assets of the enterprise. Such a structured process effectively reduces the subjectivity in the evaluation process, making the evaluation results more objective and reliable. At the same time, by using the multi-period excess return method, the free cash flow of the enterprise in the next many years can be comprehensively considered to obtain a more accurate initial value and final value of the data assets. This systematic evaluation methodology enables enterprises to make more accurate asset decisions based on scientific data analysis, thereby enhancing their competitiveness in a highly uncertain market environment. Especially for IoT enterprises, the value of data assets and their changing trends are particularly important;

[0081] Among them, the use of hierarchical analysis method to evaluate the ladder value of data assets has significant advantages, especially in systematic and quantitative decision support. The hierarchical analysis method makes the evaluation process more systematic by decomposing complex problems into multiple levels. This structured approach helps decision makers clearly understand the relationship between various factors and maintain a comprehensive perspective when considering different indicators. In addition, the hierarchical analysis method makes the evaluation process more quantitative by establishing a comparison matrix and calculating priority weights. This quantification not only improves the objectivity of the evaluation results, but also reduces subjective bias through the introduction of mathematical models, making decisions more scientific. Evaluators can use consistency tests to ensure the rationality of the constructed evaluation system and its weights, further improving the reliability of the results.

[0082] In one embodiment, the step S1 of determining the basic items of data asset evaluation including data items, data time span, and market information includes:

[0083] S11. Determine that the data items include the asset type and asset status of the enterprise, where the asset type includes fixed assets, current assets, and data assets;

[0084] S12. Determine the time span of selected data based on evaluation requirements;

[0085] S13. Determine market information including market research and customer needs and trends;

[0086] As described in the above steps S11-S13, a classification model is used to classify corporate assets into fixed assets, current assets and data assets by type. Relevant reports can be automatically generated through asset management systems such as ERP software to identify asset types and evaluate the status of assets. At the same time, interviews with stakeholders such as the financial team and data management department are conducted to clarify the purpose and requirements of the evaluation, thereby providing support for time span decisions. The time span determines the duration of data asset evaluation. In addition, for market information, a combination of quantitative and qualitative methods is used. Quantitative research includes collecting customer demand-related data through market survey questionnaires, using data analysis software such as SPSS and Excel for statistical analysis, and judging market trends. Qualitative methods include interviewing industry experts to understand current market dynamics and potential trends, and digging out in-depth information on customer needs. The above three aspects are used to determine the elements of data asset evaluation.

[0087] In one embodiment, the step S2 of determining the income period of the data asset and the free cash flow of the enterprise includes:

[0088] S21. Collect data of historical data assets and analyze the data of historical data assets through a recurrent neural network to determine the income period of the data assets;

[0089] S22. Obtain the enterprise's tax rate TR and its earnings before interest and taxes (FCF), depreciation and amortization (D&A), working capital increase (ΔWC), and capital expenditure (CAPEX) during its operations;

[0090] S23. The formula for calculating the enterprise's free cash flow FCF is:

[0091] FCF=EBIT×(1-TR)+D&A-ΔWC-CAPEX

[0092] As described in steps S21-S23 above, information on historical data assets is extracted from the company's financial management system, business operation system, and data warehouse. These historical data assets may include revenue, expenses, usage frequency, and customer feedback from the past few years. By utilizing the RNN model to process time series data, the future earnings trend of data assets can be predicted. Through feature extraction and pattern recognition of historical earnings, the long-term earnings potential of data assets can be analyzed, and the earnings period of the data assets can be determined. The latest corporate income tax rate is obtained from the company's finance department, and data is extracted through financial statements such as the income statement to clarify the company's operating capacity and obtain profit before interest and taxes. Depreciation and amortization are generally listed in the cash flow statement or notes. By calculating the changes in current assets and current liabilities, the changes in working capital during the earnings period can be determined, and capital expenditures can be extracted from the cash flow section of the investing activities of the financial statements. Finally, the company's free cash flow is obtained through comprehensive calculations.

[0093] In one embodiment, the step S3 of calculating the fixed asset contribution value and the current asset contribution value of the enterprise to obtain the data asset contribution value includes:

[0094] S31. Define the fixed asset contribution value C f , Fixed asset loss compensation F m , Return on Fixed Asset Investment F p , Depreciation of existing fixed assets F a , Depreciation of newly purchased fixed assets F n , expected fixed asset value F v , fixed asset investment rate of return F i , Current assets contribution value C m , expected current asset value M v , Return on Investment of Current Assets M r ;

[0095] S32. Fixed asset contribution value is: C f =F m +F p ;

[0096] S33, among which, F m =F a +F n, F p =F v +F i , and F i The bank loan interest rate for a preset number of years;

[0097] S34. Current assets contribution value is: C m =M v ×M r ;

[0098] S35, among which M r The bank loan interest rate is set at a predetermined number of years based on the current asset turnover cycle;

[0099] S36, based on the enterprise's free cash and fixed asset contribution value C f and current asset contribution value C m Calculate the data asset contribution value C p :

[0100] C p =FCF-C f -C m

[0101] As described in steps S31-S36 above, the fixed asset contribution value represents the contribution of fixed assets to the overall profit of the enterprise, wherein relevant indicators include fixed asset loss compensation, which is used to evaluate the portion that needs to be compensated due to wear and tear and obsolescence; fixed asset investment return, which is used to represent the income generated by fixed asset investment; existing fixed asset depreciation can be obtained by calculating the accumulated depreciation of previous fixed assets; newly purchased fixed asset depreciation can be obtained by calculating the expected depreciation of newly purchased fixed assets; expected fixed asset value represents the value that fixed assets may generate in the future; fixed asset investment rate of return is used to evaluate the rate of return of all fixed asset investments; and since the depreciation period of fixed assets other than real estate is generally Five years, so the investment rate of return selects the 5-year bank loan interest rate; the current asset contribution value can measure the impact of current assets on corporate earnings, which contains relevant indicators including the expected current asset value used to estimate the value generated by future current assets, and the current asset investment return rate represents the rate of return on current assets. Since the current asset turnover cycle is generally one accounting year, the current asset investment return rate selects the 1-year bank loan interest rate; based on the above indicators, the fixed asset contribution value and the current asset contribution value are calculated respectively, and then the data asset contribution value is calculated through the fixed asset contribution value, the current asset contribution value and the corporate free cash flow, providing a basis for the subsequent evaluation of data assets.

[0102] In one embodiment, the step S4 of calculating the discount rate of data assets using the weighted average cost of capital model and the rate of return split method includes:

[0103] S41. Weighted average cost of capital formula to calculate weighted average cost of capital:

[0104]

[0105] S42, where WACC is the weighted average cost of capital, K r is the cost of equity capital, K d is the cost of debt capital, H is the value of common stock, Z is the total amount of long-term and short-term loans, and T is the corporate income tax rate;

[0106] S43. Calculate the investment discount rate of data assets based on the weighted average cost of capital using the return split rate method:

[0107]

[0108] S44, where φ f ,φ c ,φ i are the weights of fixed assets, current assets and data assets in total assets, γ f , γ c , γ i Return on investment of fixed assets, current assets and data assets respectively

[0109] As described in the above steps S41-S44, the contribution of data assets to the enterprise is continuous. Therefore, in order to consider the time value of the economic benefits that data assets bring to the enterprise, it is necessary to determine a reasonable discount rate. First, construct a weighted average cost of capital model to calculate the weighted average cost of capital of the enterprise, and then use the return rate splitting method to calculate the discount rate of data assets. The return rate splitting method refers to the principle of reverse calculation, which removes the investment return rate of current assets and fixed assets from the weighted capital cost, and then calculates the discount rate of data assets. In actual implementation, enterprises need to regularly review and update the components of WACC, including market interest rates, tax rates, etc., to cope with changes in the economic and market environment. At the same time, for the return on investment, historical trends and market expectations can be used for analysis to make reasonable predictions in uncertainty. Furthermore, a sensitivity analysis can be performed on the calculation results to understand the impact of different assumptions on the discount rate, thereby optimizing investment decisions.

[0110] In one embodiment, the step S5 of calculating the initial value of the data asset includes:

[0111] S51. Calculate the initial value of data assets V by integrating the enterprise's free cash flow, fixed asset contribution value, current asset contribution value, data asset contribution value, and data asset investment return rate. i ;

[0112]

[0113] S52, among which V i is the initial value of data assets, (FCF-C f -C m ) t is the contribution value of data assets in year t, γ i is the data asset discount rate;

[0114] As described in the above steps S51-S52, the initial value of the data assets is obtained through the analysis of the enterprise's free cash flow, the contribution value of data assets and the discount rate of data assets in the previous part, providing a basis for the subsequent evaluation of data assets.

[0115] In one embodiment, the step S6 of calculating the ladder value of data assets using the analytic hierarchy process and based on the IoT data value assessment index system includes:

[0116] S61. Use the analytic hierarchy process to construct a data asset value assessment index system table;

[0117] S62. The evaluation index system table includes the target layer, the quasi-measurement layer, and the solution layer;

[0118] S63, where the target layer is the value of data assets, and the measurement layer includes data quality, data application, and data usage risks;

[0119] S64. Data quality includes accuracy, completeness, and data cost; data application includes scarcity, timeliness, diversity, industry characteristics, frequency of use, and versatility; and data use risks include legal restrictions and ethical constraints.

[0120] S65. Based on the evaluation indicator system, use a scale of 1 to 9 to compare each pair of indicators and construct a comparison matrix;

[0121] S66, calculating the maximum eigenvalue and consistency index θ1 of the comparison matrix;

[0122] S67, consistency index θ1 is:

[0123]

[0124] S68, where λ max is the maximum eigenvalue of the contrast matrix, n is the order of the contrast matrix;

[0125] S69, defining contrast matrices of different orders and setting a preset ratio R1;

[0126] S610, calculate the random consistency index θ using the consistency index θ1 and the preset ratio R :

[0127]

[0128] S611, if the random consistency index θ R If the consistency threshold is exceeded, it means that the evaluation index system table and comparison matrix have passed the consistency verification; otherwise, it fails and the evaluation index system table and comparison matrix are rebuilt;

[0129] S612. Calculate the indicator score and indicator weight of each indicator at the quasi-measurement layer and the solution layer using the consistency-verified evaluation indicator system table and comparison matrix.

[0130] S613. Calculate the ladder value of data assets by combining comprehensive indicator scores and indicator weights:

[0131]

[0132] S614, among which V s is the ladder value of data assets, is the weight of each indicator, S j Assign scores to each indicator;

[0133] As described in the above steps S61-S614, the hierarchical analysis method is used to first clarify the structure of the target layer, the quasi-measurement layer and the solution layer. In the solution layer, clear indicators can be considered. For example, the accuracy of data quality can include the source of the data and whether it has been cleaned. The frequency of use of data applications is the total number of times it has been used in various ways in the past 30 days. Then, according to the evaluation index system, each pair of indicators is compared using a scale of 1 to 9 to construct a comparison matrix. The rows and columns of the comparison matrix publish each indicator respectively. The scale of 1-9 is used to represent the mutual influence between the two indicators. As 1-9 increases, the relative importance also continues to rise. A consistency test is performed on the obtained comparison matrix to determine whether the evaluation index system is reasonable. In summary, the use of the hierarchical analysis method can first clarify the structure of the target layer, the quasi-measurement layer and the solution layer. The secondary analysis method obtains the total hierarchical ranking, calculates the weight of each indicator at the solution level, and ranks them relative to the indicators at the target level. In this way, it is possible to identify which solutions or indicators are relatively more important, helping decision makers focus on key factors. At the same time, when calculating weights and comparing relative importance, inconsistencies in subjective judgments may occur. Therefore, it is necessary to perform a consistency test on the generated comparison matrix to evaluate the consistency of the judgment. If the consistency test fails, it means that some judgments in the evaluation process are contradictory or unreasonable. At this time, adjustments to the hierarchical indicator system are needed. This can be achieved by reconstructing the indicator system, modifying certain values in the judgment matrix, or introducing new evaluation criteria. When the verification passes, the optimal solution for the target level, that is, the ranking of each indicator, can be obtained.

[0134] In one embodiment, the step S7 of calculating the final value of the data asset includes:

[0135] S71. Calculate the final value of data assets based on the initial value and the step-by-step value of data assets:

[0136]

[0137] S72, among which V F The ultimate value of data assets, is the data asset value correction coefficient;

[0138] As described in steps S71-S72 above, by summarizing all the above steps, the final value of the data asset is calculated using the initial value and step value of the data asset, where the data asset value correction coefficient can be set to 1. In actual implementation, the specific value depends on the external environment, market changes, industry trends and the actual performance of the data asset in a specific application scenario. It can be determined based on historical performance, industry benchmarks, market research or expert advice. When the market performance is good and the effectiveness of the data asset is fully verified, the correction coefficient can be higher than 1; in the case of uncertain or inefficient application, it can be lower than 1 or set to other reasonable values.

[0139] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An IoT data asset valuation method based on multi-period excess returns, characterized by: The basic items for determining data asset evaluation include data items, the time span of the data, and market information; Determine the revenue period of data assets and the company's free cash flow; Calculate the fixed asset contribution value and current asset contribution value of the enterprise to obtain the data asset contribution value; Calculate the discount rate of data assets using the weighted average cost of capital model and the rate of return split method; Calculate the initial value of data assets; Use the analytic hierarchy process and the IoT data value assessment index system to calculate the ladder value of data assets; Calculate the final value of data assets.

2. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for determining the basic matters of data asset evaluation, including data items, data time span, and market information, are as follows: Determine the data items including the asset type and asset status of the enterprise, where asset types include fixed assets, current assets, and data assets; Determine the time span of selected data based on evaluation requirements; Determining market information includes market research and customer needs and trends.

3. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for determining the income period of data assets and the free cash flow of the enterprise are: Collect data from historical data assets and analyze the data through recurrent neural networks to determine the return period of the data assets; Obtain the company's tax rate TR and its earnings before interest and taxes (EBIT), depreciation and amortization (D&A), working capital increase (ΔWC), and capital expenditure (CAPEX) during its operations; The formula for calculating the company's free cash flow FCF is: FCF=EBIT×(1-TR)+D&A-ΔWC-CAPEX 4. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for calculating the fixed asset contribution value and current asset contribution value of an enterprise to obtain the data asset contribution value are as follows: Define the fixed asset contribution value C f , Fixed asset loss compensation F m , Return on Fixed Asset Investment F p , Depreciation of existing fixed assets F a , Depreciation of newly purchased fixed assets F n , expected fixed asset value F v , fixed asset investment rate of return F i , Current assets contribution value C m , expected current asset value M v , Return on Investment of Current Assets M r ; The fixed asset contribution value is: C f =F m +F p ; Among them, F m =F a +F n , F p =F v +F i , and F i The bank loan interest rate for a preset number of years; The contribution of current assets is: C m =M v ×M r ; Among them, M r The bank loan interest rate is set at a predetermined number of years based on the current asset turnover cycle; Based on the enterprise's free cash and fixed asset contribution value C f and current asset contribution value C m Calculate the data asset contribution value C p : C p =FCF-C f -C m 5. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for calculating the discount rate of data assets using the weighted average cost of capital model and the rate of return split method are as follows: Weighted Average Cost of Capital Formula To calculate the weighted average cost of capital: Among them, WACC is the weighted average cost of capital, K r is the cost of equity capital, K d is the cost of debt capital, H is the value of common stock, Z is the total amount of long-term and short-term loans, and T is the corporate income tax rate; The investment discount rate of data assets is calculated based on the weighted average cost of capital using the return split rate method: where φ f ,φ c ,φ i are the weights of fixed assets, current assets and data assets in total assets, γ f , γ c , γ i They are the return on investment of fixed assets, current assets and data assets respectively.

6. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for calculating the initial value of data assets are: The initial value V of data assets is calculated by integrating the enterprise's free cash flow, fixed asset contribution value, current asset contribution value, data asset contribution value and data asset investment return rate. i ; Among them, V i is the initial value of data assets, (FCF-C f -C m ) t is the contribution value of data assets in year t, γ i is the data asset discount rate.

7. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps of using the analytic hierarchy process and calculating the ladder value of data assets based on the IoT data value assessment index system are as follows: Use the analytic hierarchy process to construct a data asset value assessment index system table; The evaluation index system table includes the target layer, the quasi-measurement layer and the solution layer; The target layer is the value of data assets, and the measurement layer includes data quality, data application, and data usage risks; Data quality includes accuracy, completeness, and data cost; data application includes scarcity, timeliness, diversity, industry characteristics, frequency of use, and versatility; and data use risks include legal restrictions and ethical constraints. According to the evaluation index system, each pair of indicators is compared using a scale of 1 to 9 to construct a comparison matrix; Calculate the maximum eigenvalue and consistency index θ1 of the comparison matrix; The consistency index θ1 is: Among them, λ max is the maximum eigenvalue of the contrast matrix, n is the order of the contrast matrix; Define contrast matrices of different orders to set preset ratios R1; Calculate the random consistency index θ using the consistency index θ1 and the preset ratio R : If the random consistency index θ R If the consistency threshold is exceeded, it means that the evaluation index system table and comparison matrix have passed the consistency verification; otherwise, it fails and the evaluation index system table and comparison matrix are rebuilt; Calculate the indicator score and indicator weight of each indicator at the quasi-measurement layer and the solution layer using the consistency-verified evaluation indicator system table and comparison matrix; Calculate the ladder value of data assets based on comprehensive indicator scores and indicator weights: Among them, V s is the ladder value of data assets, is the weight of each indicator, S j Assign a score to each indicator.

8. The method for evaluating IoT data assets based on multi-period excess returns according to claim 1, characterized in that: The steps for calculating the final value of data assets are: The final value of data assets is calculated based on the initial value of data assets and the ladder value of data assets: Among them, V F The ultimate value of data assets, It is the correction coefficient of data asset value.

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